Merge remote-tracking branch 'origin/dev-organisation' into dev-organisation

__init__.py

-from toolkit import ClassificationProject
-from compare import overlay_ROC, overlay_loss
+from .toolkit import ClassificationProject
+from .compare import overlay_ROC, overlay_loss
 

browse.py

@@ -3,6 +3,6 @@ import sys
 import numpy as np
 import matplotlib.pyplot as plt
 
-from KerasROOTClassification import ClassificationProject
+from KerasROOTClassification import *
 
 c = ClassificationProject(sys.argv[1])

toolkit.py

 #!/usr/bin/env python
 
+from sys import version_info
+
+if version_info[0] > 2:
+    raw_input = input
+
 import os
 import json
 import pickle
@@ -19,7 +24,7 @@ from sklearn.externals import joblib
 from sklearn.metrics import roc_curve, auc
 
 from keras.models import Sequential
-from keras.layers import Dense
+from keras.layers import Dense, Dropout
 from keras.models import model_from_json
 from keras.callbacks import History, EarlyStopping, CSVLogger
 from keras.optimizers import SGD
@@ -41,6 +46,7 @@ K.set_session(session)
 
 import ROOT
 
+
 class ClassificationProject(object):
 
     """Simple framework to load data from ROOT TTrees and train Keras
@@ -75,12 +81,16 @@ class ClassificationProject(object):
 
     :param nodes: number of nodes in each layer
 
+    :param dropout: dropout fraction after each hidden layer. Set to None for no Dropout
+
     :param batch_size: size of the training batches
 
     :param validation_split: split off this fraction of training events for loss evaluation
 
     :param activation_function: activation function in the hidden layers
 
+    :param activation_function_output: activation function in the output layer
+
     :param out_dir: base directory in which the project directories should be stored
 
     :param scaler_type: sklearn scaler class name to transform the data before training (options: "StandardScaler", "RobustScaler")
@@ -133,9 +143,11 @@ class ClassificationProject(object):
                         selection=None,
                         layers=3,
                         nodes=64,
+                        dropout=None,
                         batch_size=128,
                         validation_split=0.33,
                         activation_function='relu',
+                        activation_function_output='sigmoid',
                         project_dir=None,
                         scaler_type="RobustScaler",
                         step_signal=2,
@@ -155,9 +167,11 @@ class ClassificationProject(object):
         self.identifiers = identifiers
         self.layers = layers
         self.nodes = nodes
+        self.dropout = dropout
         self.batch_size = batch_size
         self.validation_split = validation_split
         self.activation_function = activation_function
+        self.activation_function_output = activation_function_output
         self.scaler_type = scaler_type
         self.step_signal = step_signal
         self.step_bkg = step_bkg
@@ -426,6 +440,19 @@ class ClassificationProject(object):
             json.dump(info, of)
 
 
+    @staticmethod
+    def query_yn(text):
+        result = None
+        while result is None:
+            input_text = raw_input(text)
+            if len(input_text) > 0:
+                if input_text.upper()[0] == "Y":
+                    result = True
+                elif input_text.upper()[0] == "N":
+                    result = False
+        return result
+
+
     @property
     def model(self):
         "Simple MLP"
@@ -439,8 +466,10 @@ class ClassificationProject(object):
             # the other hidden layers
             for layer_number in range(self.layers-1):
                 self._model.add(Dense(self.nodes, activation=self.activation_function))
+                if self.dropout is not None:
+                    self._model.add(Dropout(rate=self.dropout))
             # last layer is one neuron (binary classification)
-            self._model.add(Dense(1, activation='sigmoid'))
+            self._model.add(Dense(1, activation=self.activation_function_output))
             logger.info("Using {}(**{}) as Optimizer".format(self.optimizer, self.optimizer_opts))
             Optimizer = getattr(keras.optimizers, self.optimizer)
             optimizer = Optimizer(**self.optimizer_opts)
@@ -451,10 +480,14 @@ class ClassificationProject(object):
                                 loss='binary_crossentropy',
                                 metrics=['accuracy'])
             np.random.set_state(rn_state)
-            try:
-                self.model.load_weights(os.path.join(self.project_dir, "weights.h5"))
-                logger.info("Found and loaded previously trained weights")
-            except IOError:
+            if os.path.exists(os.path.join(self.project_dir, "weights.h5")):
+                continue_training = self.query_yn("Found previously trained weights - continue training? (Y/N) ")
+                if continue_training:
+                    self.model.load_weights(os.path.join(self.project_dir, "weights.h5"))
+                    logger.info("Found and loaded previously trained weights")
+                else:
+                    logger.info("Starting completely new model")
+            else:
                 logger.info("No weights found, starting completely new model")
 
             # dump to json for documentation